The Intersections of COVID-19, HIV, and Race/Ethnicity: Machine Learning Methods to Identify and Model Risk Factors for Severe COVID-19 in a Large U.S. National Dataset.

MLA

Kunz, Miranda, et al. “The Intersections of COVID-19, HIV, and Race/Ethnicity: Machine Learning Methods to Identify and Model Risk Factors for Severe COVID-19 in a Large U.S. National Dataset.” AIDS and Behavior, vol. 28, no. Suppl 1, Oct. 2024, pp. 5–21. EBSCOhost, https://doi.org/10.1007/s10461-024-04266-6.

APA

Kunz, M., Rott, K. W., Hurwitz, E., Kunisaki, K., Sun, J., Wilkins, K. J., Islam, J. Y., Patel, R., & Safo, S. E. (2024). The Intersections of COVID-19, HIV, and Race/Ethnicity: Machine Learning Methods to Identify and Model Risk Factors for Severe COVID-19 in a Large U.S. National Dataset. AIDS and Behavior, 28(Suppl 1), 5–21. https://doi.org/10.1007/s10461-024-04266-6

Chicago

Kunz, Miranda, Kollin W Rott, Eric Hurwitz, Ken Kunisaki, Jing Sun, Kenneth J Wilkins, Jessica Y Islam, Rena Patel, and Sandra E Safo. 2024. “The Intersections of COVID-19, HIV, and Race/Ethnicity: Machine Learning Methods to Identify and Model Risk Factors for Severe COVID-19 in a Large U.S. National Dataset.” AIDS and Behavior 28 (Suppl 1): 5–21. doi:10.1007/s10461-024-04266-6.